Back to the formula: LHC edition
While neural networks offer an attractive way to numerically encode functions, actual formulas remain the language of theoretical particle physics. We show how symbolic regression trained on matrix-element information provides, for instance, optimal LHC observables in an easily interpretable form. W...
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| Main Authors: | , , , |
|---|---|
| Format: | Article (Journal) Chapter/Article |
| Language: | English |
| Published: |
15 Nov 2021
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| In: |
Arxiv
Year: 2021, Pages: 1-29 |
| DOI: | 10.48550/arXiv.2109.10414 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.48550/arXiv.2109.10414 Verlag, lizenzpflichtig, Volltext: http://arxiv.org/abs/2109.10414 
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| Author Notes: | Anja Butter, Tilman Plehn, Nathalie Soybelman, and Johann Brehmer |
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